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Record W4361213773 · doi:10.3390/app13074253

Flexible-Imaging-Fiber-Guided Intratracheal Intubation in Rodents

2023· article· en· W4361213773 on OpenAlexaff
Sang Hoon Jeong, Cherry Kim, Hong Lee, Yoon Jeong Nam, Chil Hwan Oh, Yong-Wook Baek, Jungyun Lim, Jeong Hyeon Lee, Jaeyoung Kim

Bibliographic record

VenueApplied Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersNational Research Foundation of KoreaNational Institute of Environmental ResearchMinistry of Education, IndiaMinistry of EnvironmentKorea UniversityNational Research Foundation
KeywordsIntubationMedicineIntratracheal instillationDrugLungAnesthesiaPharmacologyBiomedical engineeringInternal medicineBronchoalveolar lavage

Abstract

fetched live from OpenAlex

Although experiments on intratracheal intubation for animals are essential for research, it remains challenging. This study aimed to validate an animal model using a flexible imaging guide system that can be conveniently and safely used as a new method to provide easy access to organs in small animals. PBS (Phosphate Buffered Saline) and PHMG (Polyhexamethylene guanidine) were administered by intratracheal intubation to 20 rodents (10 mice and 10 rats), and the changes in the lungs were observed. Results were verified using lung tissue histopathologic staining through the intratracheally administered material, which confirmed that 100% of changes in lung tissue occurred in the PHMG-injected group, where intubation was facilitated using the flexible imaging guide. The drug was conveniently and safely administered. The flexible-imaging-fiber-guide-based intratracheal drug injectable system may be conveniently used by researchers. It allows drugs to be administered quantitatively, suggesting its potential wide use in drug development and toxicity evaluation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.340
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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